DATA VISUALISATION WORKFLOW
Designing a data visualization is described as an art and science. As a science, the process requires testing, modeling, and also mathematical verification(Kennedy, Hill, Allen & Kirk, 2016). The choice of the best visual to use requires top skills, attention, and finesse. The creator needs both the technical and aesthetic detail so that the visual can be correct, simple, and beautiful. The creator needs to understand the original data and visual elements to accurately show the relationships contained in the data. The first step is normally acquiring the data that was acquired from my classmates and also my family. Data was acquired through questionnaires that were given to the respondents to answer. The respondents answered the questions voluntarily without any coercing. The next step involves exploring and cleansing of the data, this step involved checking for the data that will be used during the visualization process. I found out that there was unnecessary information that had been filled by the respondents.
The important data was recorded in an excel sheet. The other step involved identifying key information that will be used to create the visual. The key variable includes the respondent response and also the number of respondents any other information was discarded. The step involved placing variables that related to the Trump administration. The other step was to test the usability of od data. the data was placed under special heading to assist in sorting as well as segmentation. The trends were recognized and the useful data was now ready for the next final step which was building the data. the data was analyzed for accuracy and the possibility of being repeated and the potential of adapting to any changes of data which was an optional case (Kirk, 2016). The data then analyzed using the Excel spreadsheet to produce the bar graph.
Reference
Kennedy, H., Hill, R. L., Allen, W., & Kirk, A. (2016). Engaging with (big) data visualizations: Factors that affect engagement and resulting in new definitions of effectiveness. First Monday, 21(11).
Kirk, A. (2016). Data visualization: A handbook for data-driven design. Sage.